Four classifiers were used for comparing feature selection methods: Logistic Regression (LR), SVM (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM). To ensure with high probability that Random Forest uses all the features, it was tuned to have 30 trees and use 30% of attributes for each tree (\(3 \times 10^{-15}\)  chance of not selecting an attribute to any of the trees). SVM used a linear kernel and was limited to 1000 iterations, whereas all the remaining parameters were left with default scikit-learn [1]} values. The features were normalized using min-max [0-1] scaling calibrated on the training folds. Predictive performance was evaluated using: classification accuracy, the kappa statistic, macro-averaged recall, and G-mean [2]}. Results for each dataset were averaged over 10 cross-validation runs.
